activity
20242026
collaborators

6 papers

eess.AS2026

MPDR Beamforming for Almost-Cyclostationary Processes

Giovanni Bologni, Martin Bo Møller, Richard Heusdens +1

Conventional acoustic beamformers typically assume short-time stationarity and process frequency bins independently, ignoring inter-frequency correlations. This is suboptimal for a…

eess.AS2026

A two-step approach for speech enhancement in low-SNR scenarios using cyclostationary beamforming and DNNs

Giovanni Bologni, Nicolás Arrieta Larraza, Richard Heusdens +1

Deep Neural Networks (DNNs) often struggle to suppress noise at low signal-to-noise ratios (SNRs). This paper addresses speech enhancement in scenarios dominated by harmonic noise…

eess.AS2025

Harmonics to the Rescue: Why Voiced Speech is Not a Wss Process

Giovanni Bologni, Richard Heusdens, Richard C. Hendriks

Speech processing algorithms often rely on statistical knowledge of the underlying process. Despite many years of research, however, the debate on the most appropriate statistical…

eess.AS2025

Cyclic Multichannel Wiener Filter for Acoustic Beamforming

Giovanni Bologni, Richard Heusdens, Richard C. Hendriks

Acoustic beamforming models typically assume wide-sense stationarity of speech signals within short time frames. However, voiced speech is better modeled as a cyclostationary (CS)…

eess.AS2025

Wideband Relative Transfer Function (RTF) Estimation Exploiting Frequency Correlations

Giovanni Bologni, Richard C. Hendriks, Richard Heusdens

This article focuses on estimating relative transfer functions (RTFs) for beamforming applications. Traditional methods often assume that spectra are uncorrelated, an assumption th…

eess.SP2024

Optimal Pilot Design for OTFS in Linear Time-Varying Channels

Ids van der Werf, Richard Heusdens, Richard C. Hendriks +1

This paper investigates the positioning of the pilot symbols, as well as the power distribution between the pilot and the communication symbols in the orthogonal time frequency spa…